Method and apparatus for performing irradiation time optimization for intensity modulated proton therapy during treatment planning while maintaining acceptable irradiation plan quality

ABSTRACT

A computer implemented method of determining a resultant treatment plan for a proton radiation therapy system based on given dose volume constraints, wherein the resultant treatment plan is optimized for treatment time comprises accessing the dose volume constraints and range information, wherein the range information indicates acceptable deviations from the dose volume constraints. Based on the proton radiation therapy system, the method further comprises accessing machine configuration information comprising a plurality of machine parameters that define a maximum resolution achievable in irradiating a patient. Further, the method comprises iteratively adjusting the plurality of machine parameters to values which decrease the maximum resolution and simulating a plurality of candidate treatment plans to generate a plurality of treatment plan results, wherein each treatment plan result comprises: a respective treatment time and a respective plan quality. Finally, the method comprises selecting a resultant treatment plan with the shortest treatment time.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation of, claims the benefit of andpriority to U.S. application Ser. No. 16/989,618, filed Aug. 10, 2020,entitled “METHOD AND APPARATUS FOR PERFORMING IRRADIATION TIMEOPTIMIZATION FOR INTENSITY MODULATED PROTON THERAPY DURING TREATMENTPLANNING WHILE MAINTAINING ACCEPTABLE IRRADIATION PLAN QUALITY,” whichclaims the benefit of and priority to U.S. application Ser. No.16/147,110, filed Sep. 28, 2018, entitled “METHOD AND APPARATUS FORPERFORMING IRRADIATION TIME OPTIMIZATION FOR INTENSITY MODULATED PROTONTHERAPY DURING TREATMENT PLANNING WHILE MAINTAINING ACCEPTABLEIRRADIATION PLAN QUALITY” and hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

This description relates generally to the field of radiation therapy,and more particularly to optimizing performance of the therapy system inthe execution of a radiation therapy treatment plan while maintainingacceptable plan quality.

BACKGROUND

Radiation therapy treatment plan development generally employs medicalimaging, such as X-ray, computed tomography (CT), magnetic resonanceimaging (MRI), or the like. Typically, a series of two-dimensionalpatient images, each representing a two-dimensional cross-sectional“slice” of the patient anatomy, are used to reconstruct athree-dimensional representation of a volume of interest (VOI), orstructure of interest, from the patient anatomy.

The VOI typically includes one or more organs of interest, oftenincluding a planning target volume (PTV), such as a malignant growth oran organ including malignant tissue targeted for radiation therapy; arelatively healthy organ at risk (OAR) in the vicinity of a malignantgrowth at risk of radiation therapy exposure; or a larger portion of thepatient anatomy that includes a combination of one or more PTVs alongwith one or more OARs. The objective of the radiation therapy treatmentplan development typically aims to irradiate as much of the PTV as nearthe prescription dose as possible, while attempting to minimizeirradiation of nearby OARs.

The resulting radiation therapy treatment plans are used during medicalprocedures to selectively expose precise areas of the body, such asmalignant tumors, to specific doses of radiation in order to destroy theundesirable tissues. During the development of a patient-specificradiation therapy treatment plan, information generally is extractedfrom the three-dimensional model to determine parameters such as theshape, volume, location, and orientation of one or more PTVs along withone or more OARs.

Proton therapy is a type of external beam radiation therapy that ischaracterized by the use of a beam of protons to irradiate diseasedtissue. Typically, radiation therapy involves directing a beam of highenergy proton, photon, or electron radiation (“therapeutic radiation”)into a target volume (e.g., a tumor or lesion). A chief advantage ofproton therapy over other conventional therapies such as X-ray orneutron radiation therapies is that proton radiation can be limited bydepth, and therefore the exposure to inadvertent radiation can beavoided or at least limited by non-target cells having a depth beyond atarget calculated area.

A popular implementation of proton therapy uses mono-energetic pencilbeams at varying energy levels, which are spot-scanned over a targetarea for one or more layers of depth. By superposition of several protonbeams of different energies, a Bragg peak can be spread out to covertarget volumes using a uniform, prescribed dose. This enables protonradiation applications to more precisely localize the radiation dosagerelative to other types of external beam radiotherapy. During protontherapy treatment, a particle accelerator such as a cyclotron orsynchrotron is used to generate a beam of protons from, for example, aninternal ion source located in the center of the particle accelerator.The protons in the beam are accelerated (via a generated electricfield), and the beam of accelerated protons is subsequently “extracted”and magnetically directed through a series of interconnecting tubes(called a beamline), often through multiple chambers, rooms, or evenfloors of a building, before finally being applied through a radiationapplication device at an end section of beam line (often through aradiation nozzle) to a target volume in a treatment room.

As the volumes (e.g., organs, or regions of a body) targeted forradiation therapy are often below the surface of the skin and/or extendin three dimensions, and since proton therapy—like all radiationtherapies—can be harmful to intervening tissue located in a subjectbetween the target area and the beam emitter, the precise calculationand application of correct dosage amounts and positions are critical toavoid exposing regions in the radiation subject outside the specificareas targeted to receive radiation.

Before a patient is treated with radiation, a treatment plan specific tothat patient is developed. The plan defines various aspects of thetherapy using simulations and optimizations based on past experiences.For example, for intensity modulated radiation therapy (IMRT), the plancan specify the appropriate beam type and the appropriate beam energy.Other parts of the plan can specify, for example, the angle of the beamrelative to the patient, the beam shape, the placement of boluses andshields, and the like. In general, the purpose of the treatment plan isto deliver sufficient radiation to the target volume while minimizingexposure of surrounding healthy tissue to the radiation.

In IMRT, the planner's goal is to find a solution that is optimal withrespect to multiple clinical goals that may be contradictory in thesense that an improvement toward one goal may have a detrimental effecton reaching another goal. For example, a treatment plan that spares theliver from receiving a dose of radiation may result in the stomachreceiving too much radiation. These types of tradeoffs lead to aniterative process in which the planner creates different plans to findthe one plan that is best suited to achieving the desired outcome.Furthermore, treatment planning software can be used to find an optimalplan that considers all the clinical goals and dosimetric criteria.

In proton therapy, short irradiation times are desirable. Patients musthold their breath during therapy on certain organs, particularly theirlungs, to avoid the tumor moving in and out of the proton beam.Therefore, lung or liver cancer is typically treated with breath-holdingtechniques so as to minimize the interplay effects of moving targets.Delivering the required dose as quickly as possible therefore limits theamount of time that a patient needs to hold her breath.

One of the drawbacks of conventional commercially available treatmentplanning systems is that while they may, for example, be optimizedaccording to certain dose volume constraints (for target volume andorgans at risk) or according to plan robustness (to take inter orintra-fraction position inaccuracies into account), typically,conventional proton therapy systems do not consider temporal behavior ofthe beam application during optimization. That is to say, treatmentplans are currently optimized for plan quality, e.g., optimizedaccording to given dose volume constraints for target volume and organsat risk. More specifically, conventional treatment planning systems forproton therapy do not optimize for time by taking into account certainmachine specific criteria (related to the proton therapy delivery systemproperties) that have a significant impact on irradiation times. Inother words, conventional treatment planning systems do not optimize fordelivery system machine-specific limitations—this results in prolongingirradiation times and increasing the probability of an interlockoccurrence.

SUMMARY

Embodiments according to the present invention provide a methodologythat performs time-based optimization during the course of treatmentplanning for intensity modulated proton therapy systems, in particular,by taking into account certain beam characteristics and configuration(or calibration) criteria pertaining to the physical constraints ofmachines delivering the proton therapy. In other words, embodimentsaccording to the present invention create a treatment plan that isoptimized for efficiency of performance (using certain radiation therapybeam and machine specific parameters) while simultaneously delivering aclinically acceptable plan quality (for dose distribution).

In one embodiment, a computer implemented method of determining aresultant treatment plan for a proton radiation therapy system based ongiven dose volume constraints, wherein the resultant treatment plan isoptimized for treatment time is disclosed. The method comprisesaccessing the dose volume constraints and range information, wherein therange information indicates acceptable deviations from the dose volumeconstraints. Based on the proton radiation therapy system, the methodalso comprises accessing machine configuration information comprising aplurality of machine parameters that define a maximum resolutionachievable by the proton radiation therapy system in irradiating apatient. Further, the method comprises iteratively adjusting theplurality of machine parameters to generate a plurality of candidatetreatment plans, wherein the iteratively adjusting comprises adjustingthe plurality of machine parameters to values which decrease the maximumresolution. Subsequently, the method comprises simulating the pluralityof candidate treatment plans with respect to the proton radiationtherapy system to generate a plurality of treatment plan results,wherein each treatment plan result comprises a respective treatment timeand a respective plan quality. Finally, the method comprises selectingthe resultant treatment plan from the plurality of candidate treatmentplans, wherein the resultant treatment plan yields a treatment planresult comprising a shortest treatment time of the plurality oftreatment plan results and an acceptable plan quality with respect tothe dose volume constraints. Embodiments also include a computer systemimplemented to execute the method as described above.

Thus, embodiments according to the invention improve the field ofradiation treatment planning specifically and the field of radiationtherapy in general. In IMRT, beam intensity is varied across eachtreatment region (target volume) in a patient. Instead of being treatedwith a relatively large and uniform beam, the patient is treated withmany smaller beams (e.g., pencil beams or beamlets), each of which canhave its own intensity, and each of which can be delivered from adifferent angle (which may be referred to as beam geometry) to irradiatea spot. Because of the many possible beam geometries, the number ofbeams, and the range of beam intensities, there is effectively aninfinite number of potential treatment plans, and therefore consistentlyand efficiently generating and evaluating high-quality treatment plansis beyond the capability of a human and requires the use of a computingsystem, particularly considering the time constraints associated withthe use of radiation therapy to treat ailments like cancer, andparticularly considering the large number of patients that areundergoing or need to undergo radiation therapy during any given timeperiod.

Furthermore, performing a multi-directional optimization that optimizesa treatment plan for efficiency by considering complex machine andbeam-specific parameters (e.g., minimal application monitor units perspot, energy layer spacing, spot size and spot spacing, etc.) withoutsacrificing plan quality is beyond the capability of a human andrequires the use of a computing system. Embodiments according to theinvention allow effective treatment plans with low treatment deliverytimes to be generated, which limit the possibility of irregularities orinaccuracies in treatment delivery due to patient movement. Also,embodiments according to the invention help improve the functioning ofcomputing systems by improving system reliability and availability,which results from a lower likelihood of interlock occurrence.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an example of a computing system uponwhich the embodiments described herein may be implemented.

FIG. 2 illustrates an embodiment of a knowledge-based planning systemincorporating a combination of dosimetric criteria and certain deliverysystem properties for generating radiation treatment plans in anembodiment according to the present invention.

FIG. 3A illustrates a data flow diagram of a process that can beimplemented to create a treatment plan that takes into considerationcertain physical degrees of freedom in an embodiment according to thepresent invention.

FIG. 3B illustrates a data flow diagram of a process that can beimplemented to select a resultant treatment plans from a number ofcandidate treatment plants generated by varying machine specificparameters in accordance with an embodiment of the present invention.

FIG. 4 illustrates the manner in which the resultant energies and stepsbetween them can be optimized for in accordance with an embodiment ofthe present invention.

FIGS. 5A and 5B illustrate exemplary grids in the x-y plane over whichproton therapy is delivered in accordance with an embodiment of theinvention.

FIG. 6 is a table illustrating a plurality of candidate treatment plansand their results, wherein each cell of the table is representative of aradiation machine model implemented by the optimization engine todetermine irradiation time and plan quality for a respective treatmentplan in accordance with an embodiment of the invention.

FIG. 7 is a high-level software flow diagram illustrating the manner inwhich machine specific parameters are used in the treatment planningsystem to determine the most efficient treatment plans in accordancewith an embodiment of the present invention.

FIG. 8 is a flowchart depicting another exemplary process flow fordetermining a resultant treatment plan for a proton radiation therapysystem based on given dose volume constraints, wherein the resultantplan is optimized for treatment time, in accordance with an embodimentof the present invention.

DETAILED DESCRIPTION

Reference will now be made in detail to the various embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. While described in conjunction with theseembodiments, it will be understood that they are not intended to limitthe disclosure to these embodiments. On the contrary, the disclosure isintended to cover alternatives, modifications and equivalents, which maybe included within the spirit and scope of the disclosure as defined bythe appended claims. Furthermore, in the following detailed descriptionof the present disclosure, numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure.However, it will be understood that the present disclosure may bepracticed without these specific details. In other instances, well-knownmethods, procedures, components, and circuits have not been described indetail so as not to unnecessarily obscure aspects of the presentdisclosure.

Some portions of the detailed descriptions that follow are presented interms of procedures, logic blocks, processing, and other symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the means used by thoseskilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. In the presentapplication, a procedure, logic block, process, or the like, isconceived to be a self-consistent sequence of steps or instructionsleading to a desired result. The steps are those utilizing physicalmanipulations of physical quantities. Usually, although not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated in a computing system. It has proven convenient at times,principally for reasons of common usage, to refer to these signals astransactions, bits, values, elements, symbols, characters, samples,pixels, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present disclosure,discussions utilizing terms such as “accessing,” “adjusting,”“simulating,” “selecting,” “loading,” and “using or the like, refer toactions and processes (e.g., the flowcharts of FIG. 8) of a computingsystem or similar electronic computing device or processor (e.g., thecomputing system 100 of FIG. 1). The computing system or similarelectronic computing device manipulates and transforms data representedas physical (electronic) quantities within the computing systemmemories, registers or other such information storage, transmission ordisplay devices.

Portions of the detailed description that follows are presented anddiscussed in terms of a method. Although steps and sequencing thereofare disclosed in figures herein (e.g., FIG. 8) describing the operationsof this method, such steps and sequencing are exemplary. Embodiments arewell suited to performing various other steps or variations of the stepsrecited in the flowchart of the figure herein, and in a sequence otherthan that depicted and described herein.

Embodiments described herein may be discussed in the general context ofcomputer-executable instructions residing on some form ofcomputer-readable storage medium, such as program modules, executed byone or more computers or other devices. By way of example, and notlimitation, computer-readable storage media may comprise non-transitorycomputer storage media and communication media. Generally, programmodules include routines, programs, objects, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. The functionality of the program modules may becombined or distributed as desired in various embodiments.

Computer storage media includes volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, random access memory (RAM), read only memory (ROM),electrically erasable programmable ROM (EEPROM), flash memory or othermemory technology, compact disk ROM (CD-ROM), digital versatile disks(DVDs) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to store the desired information and that canaccessed to retrieve that information.

Communication media can embody computer-executable instructions, datastructures, and program modules, and includes any information deliverymedia. By way of example, and not limitation, communication mediaincludes wired media such as a wired network or direct-wired connection,and wireless media such as acoustic, radio frequency (RF), infrared andother wireless media. Combinations of any of the above can also beincluded within the scope of computer-readable media.

This present disclosures provides a solution to the challenge inherentof reducing irradiation time during treatment delivery withoutsacrificing dose distribution (or treatment plan quality). Inparticular, various embodiments of the present disclosure provide amethodology that performs time-based optimization during the course oftreatment planning for proton therapy systems, in particular, by takinginto account characteristics of the proton delivery system (e.g.,certain beam characteristics and configuration/calibration criteriapertaining to machines delivering the proton therapy.) In other words,embodiments according to the present invention create a treatment planthat is optimized for efficiency of performance (using certain radiationtherapy beam and machine specific parameters) while simultaneouslydelivering a clinically acceptable plan quality (for dose distribution).

Conventional commercially available radiation treatment planning systemscan be optimized according to given dose volume constraints for planningtarget volume (PTV) and organs at risk (OAR) and according to planrobustness (taking inter or intra-fraction position inaccuracies intoaccount). Conventional treatment planning systems, however, do not takeinto account certain physical constraints or properties of the deliverysystem (e.g., certain beam characteristics or machine specific operationparameters and their limits) in the base data it considers in theperformance of this optimization. Accordingly, even though conventionaltreatment planning systems may be able to produce a clinicallyacceptable treatment plan, the treatment plan developed may not use thefull system or machine capability to make use of the system in the mostefficient or reliable way to get the treatment delivered as planned. Inother words, because certain delivery system specific parameters (e.g.,pertaining to physical constraints of the beam machine) are fixed (andcannot be varied during treatment planning or while trying to establishthe most optimal treatment plan), the treatment plans developed byconventional treatment planning systems, while being acceptable, may notbe optimized for time and may, therefore, be inefficient. Accordingly,even if an optimized treatment plan in conventional systems passes thecriteria for plan quality and treatment delivery time, the applicationat the radiation therapy beam machine may fail during delivery or maynot have the optimal delivery efficiency as requested by plan objectivesduring treatment planning due to limitations specific to the beammachine capability (or machine-specific plan parameters) that are nottaken into account by the currently available commercial treatmentplanning systems.

Accordingly, embodiments of the present invention account for and adjustcertain delivery system specific parameters, e.g., beam energies, spotpositions, global minimum spot intensities, and spot lateral spread (orspot size) in developing a performance optimized treatment plan thatdeliver the required plan quality. This allows embodiments of thepresent invention to perform a multi-directional optimization anddeliver a treatment plan that simultaneously delivers a clinicallyacceptable plan quality (for dose distribution) with the shortestirradiation periods. By comparison, conventional treatment deliverysystems, parameters associated with certain physical constraints, e.g.,beam energies, spot positions, spot lateral spread, etc., are assumed tobe fixed and cannot be optimized for in the development of a treatmentplan.

In one embodiment, the present invention comprises a software basedoptimization engine that translates delivery system knowledge intotreatment planning. The optimization engine provides a treatment planwith acceptable treatment plan quality (dose distribution) whileoptimizing for plan delivery efficiency (especially delivery time forall fields of a treatment plan) as well as delivery performance ingeneral to improve system reliability and availability. Optimizing forefficiency (by taking into consideration radiation therapy beam machinespecific behavior, thresholds and limitations) also reduces theprobability of interlock occurrence. Accordingly, embodiments of thepresent invention are able to deliver treatment plans with the fullcapability of the treatment delivery system (TDS) with the highestpossible efficiency. The optimization engine allows users of the TDS totake advantage of the radiation treatment delivery system and treatmentplanning system in a targeted manner by finding an optimal outcome forindividual treatment (with respect to efficiency influencing variablespertaining to treatment delivery).

Embodiments of the present invention are advantageous because they allowoptimal usage of machine capability—this reduces interlock occurrence,which can be managed and directly influenced during treatment planning.As a result of fewer interlocks, the delivery system is able to keep upwith a created treatment plan and machine downtimes are avoided.

Further, the optimization engine of the present invention simultaneouslyensures adequate treatment plan quality and tailors treatment deliveryto take advantage of the maximum system capabilities of a TDS (includingtemporal behavior and reliability). Accordingly, embodiments of thepresent invention minimize overall irradiation times while maintaininglimits that guarantee accurate beam position and dose application.Because treatment times are shorter, embodiments of the presentinvention support advanced breath-hold treatment planning. In otherwords, with shorter treatment times, the patient may not be required tohold their breath for longer periods of time, thereby, allowing thetreatment to be delivered accurately while minimizing patient movement.Furthermore, machine load/wear is reduced because of the shorterirradiation times.

Additionally, optimizing for certain delivery system specificparameters, e.g., beam energies, spot positions, global minimum spotintensities, and spot lateral spread (or spot size) leads to more robusttreatment plans. For example, varying spot positions and spot lateralspread may allow an acceptable dose to be applied using fewer spots.Optimizing over additional physical degrees of freedom may also lead tobetter results for the dosimetric criteria due to a qualitatively largerset of degrees of freedom available to the optimization engine. Inparticular, it leads to improved dose homogeneity for the targetstructure, e.g., the tumor.

Designing a more reliable system by taking certain machine degrees offreedom (e.g., beam energies, spot positions, global minimum spotintensities, and spot lateral spread) into consideration also preventsthe designer of a treatment plan from having to re-design a treatmentplan in case a prior one fails, e.g., as a result of an interlock. Byproviding increased degrees of freedom in the optimization process andmaking more optimal use of hardware capabilities, embodiments of thepresent invention are able to reduce delivery time, machine interlockprobability and machine maintenance, without having a significant impacton the primary dosimetric objectives.

FIG. 1 shows a block diagram of an example of a computing system 100upon which the embodiments described herein may be implemented. In itsmost basic configuration, the system 100 includes at least oneprocessing unit 102 and memory 104. This most basic configuration isillustrated in FIG. 1 by dashed line 106. The system 100 may also haveadditional features and/or functionality. For example, the system 100may also include additional storage (removable and/or non-removable)including, but not limited to, magnetic or optical disks or tape. Suchadditional storage is illustrated in FIG. 1 by removable storage 108 andnon-removable storage 120. The system 100 may also containcommunications connection(s) 122 that allow the device to communicatewith other devices, e.g., in a networked environment using logicalconnections to one or more remote computers.

The system 100 also includes input device(s) 124 such as keyboard,mouse, pen, voice input device, touch input device, etc. Outputdevice(s) 126 such as a display device, speakers, printer, etc., mayalso be included.

As will be explained further below, embodiments according to theinvention utilize an optimization engine 218. In the example of FIG. 1,the memory 104 includes computer-readable instructions, data structures,program modules, and the like associated with the optimization engine218. However, the optimization engine 218 may instead reside in any oneof the computer storage media used by the system 100, or may bedistributed over some combination of the computer storage media, or maybe distributed over some combination of networked computers.

The optimization engine 218 is programmed to simultaneously optimize forperformance efficiency while also taking into consideration treatmentplan quality and tailor treatment delivery to take advantage of themaximum system capabilities of a TDS (including temporal behavior andreliability). In conventional treatment planning systems, the physicaldegrees of freedom specific to a machine, e.g., the number of availablebeam energies and steps between them, spot positions, spot lateralspread, etc. are fixed parameters. The optimization engine 218 allowsthe user to optimize for efficiency by varying physical degrees offreedom pertaining to the machine. By comparison, conventional treatmentplanning systems only use fixed parameters (that may be developedthrough trial and error) for the physical degrees of freedom e.g., thenumber of available beam energies and steps between them, spotpositions, spot lateral spread, etc.

FIG. 2 illustrates an embodiment of a knowledge-based planning system200 incorporating a combination of dosimetric criteria and certaindelivery system properties for generating radiation treatment plans inan embodiment according to the present invention. In the example of FIG.2, the system 200 includes a knowledge base 202 and a treatment planningtool set 210. The knowledge base 202 includes patient records 204 (e.g.,radiation treatment plans), treatment types 206, statistical models 208and other dosimetric criteria for delivering an effective treatmentplan. The knowledge base 202, in accordance with embodiments of thepresent invention, may also include certain delivery system properties238 (e.g., the degrees of freedom that can be varied including thenumber of available energies and the steps between them, spot positions,spot intensities and spot lateral spread).

The treatment planning tool set 210 in the example of FIG. 2 includes acurrent patient record 212, a treatment type 214, a medical imageprocessing module 216, an optimization engine 218, a dose distributionmodule 220, and a final radiation treatment plan 222.

The treatment planning tool set 210 searches through the knowledge base202 (through the patient records 204) for prior patient records that aresimilar to the current patient record 212. The statistical models 208can be used to compare the predicted results for the current patientrecord 212 to a statistical patient. Using the current patient record212, a selected treatment type 206, selected statistical models 208, anddelivery system properties 238, the tool set 210 generates a radiationtreatment plan 222 using the optimization engine 218 to optimize forseveral (potentially conflicting) objectives, e.g., time versus adequacyof dose distribution. A radiation treatment plan developed in thismanner (e.g., the treatment plan 222) can be referred to as a balancedplan.

More specifically, based on past clinical experience, when a patientpresents with a particular diagnosis, stage, age, weight, sex,co-morbidities, etc., there can be a treatment type that is used mostoften. By selecting the treatment type that the planner has used in thepast for similar patients, a treatment type 214 can be chosen. Themedical image processing module 216 provides automatic contouring andautomatic segmentation of two-dimensional cross-sectional slides (e.g.,from computed tomography or magnetic resonance imaging) to form a 3Dimage using the medical images in the current patient record 212. Dosedistribution maps are calculated by the dose distribution module 220.

The knowledge base 202 can be searched for a combination of objectivesthat can be applied by the optimization engine 218 to determine a dosedistribution. For example, an average organ-at-risk dose-volumehistogram, a mean cohort organ-at-risk dose-volume histogram, andaverage organ-at-risk objectives can be selected from the knowledge base202. In embodiments according to the present invention, the optimizationengine 218 can optimize for certain delivery system specific parameters(from delivery system properties 238), e.g., the number of availableenergies and the steps between them, spot positions, spot intensitiesand spot lateral spread. As mentioned above, optimizing over additionalphysical degrees of freedom may also lead to better results for thedosimetric criteria or clinical goals due to a qualitatively larger setof degrees of freedom available to the optimization engine. Inparticular, it leads to improved dose homogeneity for the targetstructure, e.g., the tumor. Further, it leads to time optimization—anacceptable dose quality can be delivered in the fastest and mostefficient manner possible.

By comparison, in conventional therapy systems, the only criteriaconsidered were the static properties of the final dose distribution,which in effect determined the quality of the plan. Embodiments of thepresent invention, however, consider the time aspect of delivering thedose distribution as well. In other words, embodiments of the presentinvention enable an acceptable quality of treatment plan to be deliveredin the most time efficient way.

FIG. 3A illustrates a process 300 that can be implemented to create atreatment plan that takes into consideration certain physical degrees offreedom in an embodiment according to the present invention. Process 300can be implemented as computer-readable instructions stored in acomputer-usable medium and executed on a computing system like thesystem 100 of FIG. 1.

The clinical goals 320 of FIG. 3A include (as computer-readable data) aclinical goal or set of clinical goals. In general, a clinical goal is afactor that is related to treatment outcome. The clinical goals offerleeway in the trade-off between the competing objectives of deliveringdoses to a target volume (e.g., diseased tissue) while minimizing dosesto surrounding (e.g., healthy) tissue. Clinical goals 320 may alsoinclude an acceptable range of deviation from those goals and stillyield an acceptable plan quality.

The clinical goals 320 are used to guide the development of a radiationtreatment plan describing, among other parameters, the type of radiationto be used, the orientation of the radiation therapy beams to bedirected toward patient at multiple beam stations, the shape forcollimation of the beams, and the amount of dose to be delivered at eachstation. A clinical goal may also define constraints or goals forquality metrics such as minimum and maximum dose amounts and mean dosefor particular tissue volumes (called regions of interest or ROIs), dosehomogeneity, target volume dose distribution, organ-at-risk dosedistributions, other normal tissue dose distributions, other spatialdose distributions, and other acceptable ranges of deviation.

Given the patient anatomy details 310 (also available in the knowledgebase 202) and the clinical goals 320, the treatment planning system canoptimize for the dosimetric criteria. For example, a dosimetriccriterion may dictate a minimum number of Grays of radiation to beapplied to a planning target volume (PTV) and a maximum number of Graysof radiation to be applied to an organ at risk (OAR). In other words,the dosimetric criteria may be considered a dose volume plan or dosedistribution plan that determines the manner in which the dose isdistributed over the three dimensional space being treated.

Embodiments of the present invention comprise an optimization engine 218that delivers an acceptable treatment plan quality (for a givendosimetric criteria) while optimizing for delivery system properties 330(e.g., various degrees of physical freedom associated with a treatmentdelivery system) to develop a treatment plan 340. As mentioned above,conventional treatment systems were only able to optimize for thedosimetric criteria. By comparison, embodiments of the present inventionallow the user to optimize for efficiency by varying physical degrees offreedom pertaining to the machine, e.g., the number of available beamenergies and steps between them, spot positions, spot lateral spread,etc.

In one embodiment, the treatment planning software may receive as inputsmachine configuration such as the maximum and minimum range for each ofthe various machine specific parameters, e.g., a maximum and minimumdepth of the target structure for determining layer spacing, a maximumand minimum number of monitor units (MUs) per spot that a beam machinecan deliver, a maximum and minimum spot size (or spot lateral spread),and a maximum and minimum breadth of the target structure fordetermining spot positioning. Given the ranges for each of theparameters, the optimization engine 218 is able to perform simulationsand employ some complex optimization algorithms to deliver an optimaltreatment plan.

In one embodiment, the range information provided to the treatmentplanning software may be acceptable deviations from the dose volumeconstraints. For example, the PVT coverage may be between 95% and 107%and dose maximum below 112%.

In one embodiment, for example, the optimization engine 218 of thetreatment planning software may run simulations to determine variousirradiation times associated with a selected set of machine specificparameters. The optimization engine 218 may be programmed to determinethe manner in which to vary the machine specific parameters in order tosolve for the lowest irradiation times while maintaining an acceptableplan quality. In other words, the optimization engine 218 may beprogrammed to analyze the intermediate results and use them to morequickly converge towards a solution that yields the lowest irradiationtimes. In one embodiment, the optimization engine 218 may be programmedwith one or several different optimization algorithms that allow theengine to converge to the most efficient solution without sacrificingplan quality.

In one embodiment of the present invention, based on the protonradiation therapy system, the machine configuration informationincluding various machine parameters are accessed. The machineparameters may, for example, define a maximum resolution achievable bythe proton radiation therapy system in irradiating a patient. Theoptimization engine, in one embodiment, may be configured to iterativelyadjust the various machine parameters to generate one or more candidatetreatment plans. The machine parameters can, for instance, beiteratively adjusted to values that decrease the maximum resolution.Subsequently, the various candidate treatment plans generated can besimulated in order to determine a respective treatment time andrespective plan quality associated with each of the treatment plans. Theoptimization engine may then be programmed to select a candidatetreatment plan that may yield an acceptable plan quality and theshortest possible treatment time.

FIG. 3B illustrates a data flow diagram of a process that can beimplemented to select a resultant treatment plans from a number ofcandidate treatment plants generated by varying machine specificparameters in accordance with an embodiment of the present invention.

The various degrees of freedom to vary 352 and the acceptable high lowranges for each parameter 354 are used to generate a set of candidatetreatment plans 356. Each candidate treatment plan may have a differentvalue for each of the machine specific parameters (or degrees offreedom) but within a particular candidate treatment plan the valuewould be remain constant. For example, three different candidatetreatment plans may be generated, where each of the candidate treatmentplans would have a different value for the minimum number of MUs perspot, e.g, 3, 5 and 7 respectively. The simulator 362 of theoptimization engine will subsequently generate a treatment plan result386 for one or more of the candidate treatment plans. In one embodiment,the optimization engine may be able to converge on the most efficientsolution without needing to simulate each and every one of the candidatetreatment plans. Given the dosimetric criteria 370, the treatment planselector 380 will then select the most time-efficient treatment plan 382from amongst the result set.

For example, embodiments of the present invention allow the globalminimum spot intensity (measured in Monitor Units or MUs) to beconsidered by the optimization process. In conventional systems, theglobal minimum spot intensity was fixed based on the beam machine.Embodiments of the present invention allow the minimum MUs per spot tovary in order to optimize for time constraints. For example, in someparts of the body it is beneficial to place spots that only deliver theminimum MUs. In other parts, however, placing many spots with lowintensity confers no benefit with respect to dosimetric criteria overplacing fewer spots with higher intensity. In other words, it may befaster and more efficient for some parts of the body to receive fewerspots with higher intensity rather than several spots with lowintensity. For certain parts of the body, the treatment delivery systemmay use fewer spots with more MUs per spot—sacrificing resolution, whilemaintaining treatment plan quality allows the system to be moreefficient.

By allowing the minimum MUs per spot to vary, the optimization engine218 can optimize the treatment plan by delivering higher intensity atfewer spots on a patient (where permissible) rather than severaldifferent spots at lower intensity. Further, by reducing the totalnumber of spots, the optimization engine can develop treatment planswith faster irradiation times and also improved dose homogeneity for thetarget structure, e.g., the tumor. For example, referring to the exampleprovided above, if the minimum number of monitor units (MUs) per spot isspecified to be 5 with a range of plus or minus 3, then the optimizationengine may generate a number of candidate treatment plans where theminimum MUs per spot is varied from 2 MU to 8 MU between the variouscandidate treatment plans. After simulating the various treatment plans,the optimization engine may select the treatment plan that generatesacceptable plan quality with the fastest possible treatment time.

FIG. 4 illustrates the manner in which the resultant energies and stepsbetween them can be optimized for in accordance with an embodiment ofthe present invention.

In IMRT, beam intensity is varied across each treatment region (targetvolume) in a patient. Instead of being treated with a relatively largeand uniform beam, the patient is treated with many smaller beams (e.g.,pencil beams or beamlets), each of which can have its own intensity, andeach of which can be delivered from a different angle (which may bereferred to as beam geometry). Because of the many possible beamgeometries, the number of beams, and the range of beam intensities,there can effectively be an innumerable amount of potential treatmentplans. In developing treatment plans, embodiments of the presentinvention use optimization engine 218 to select a treatment plan havingthe most efficient performance without sacrificing plan quality asmentioned above.

One of the beam machine specific parameters that can be optimized inaccordance with an embodiment of the present invention is number of usedenergies and the spacing or distance between them. The energy levelcorresponds to the depth within the treatment region that the beam canreach. For example, an 180 MeV beam is able to irradiate a PTV at ahigher depth than a 150 MeV beam. In conventional systems, the energydifference between adjacent/consecutive layers are fixed prior to anyoptimization using some scheme such as a fixed number of layers or afixed layer spacing (which is the difference between consecutiveenergies, e.g., 3 MeV between each layer).

In one embodiment of the present invention, for example, the number ofused energies and differences between consecutive energies can beconsidered as an additional parameter that can be varied between variouscandidate treatment plans. In other words, individual candidatetreatment plans can have differences between energy levels in a deliverytreatment field. As mentioned above, proton therapy can be limited bydepth and, therefore, exposure to inadvertent radiation can be avoidedor at least limited by non-target cells having a depth beyond a targetcalculated area. As mentioned above, in conventional treatment systems,the energy layer spacing (the difference between consecutive energylayers) was a fixed parameter and was associated with a fixed distancebetween each layer. For example, in a conventional treatment system, ifthe minimum depth of a target structure was 100 MeV and the maximumdepth of the target structure was 200 MeV with a fixed 5 MeV layerspacing, then the treatment delivery system may deliver treatment ateach 5 MeV increment between 100 MeV and 200 MeV (e.g., at 105 MeV, 110MeV, 115 MeV, etc.)

By comparison, embodiments of the present invention allow theoptimization engine to determine the number of energy levels (that needto be added between the maximum and minimum depth of the targetstructure) and the differences between consecutive energy levels (in adelivery treatment field) between them. For example, the treatmentplanning software may receive the minimum and maximum depth of treatment(based on the patient). Alternatively, the treatment planning softwaremay also receive a range of acceptable energies. As shown in FIG. 4, thePTV may be between the ranges of 220 MeV and 182 MeV. Knowing theminimum and maximum depth (or acceptable range of energies), and theminimum and maximum number of energy layers it can deposit, thetreatment planning software may generate a number of candidate treatmentplans with varying depths and number of energy layers. Subsequently, theoptimization engine may simulate one or more of the various candidatetreatment plans and automatically perform an optimization to determinethe optimal number of layers and the space between them. For example,for the PTV shown in FIG. 4, the optimization engine 218 may determinethat the optimum number of layers is 4, spaced at 210 MeV, 205 MeV, 195MeV and 190 MeV. Accordingly, as compared with conventional treatmentplanning systems, embodiments of the present invention are notrestricted to a fixed number of layers or a fixed space between thelayers (which can be inordinately time-intensive). The number of layersand the distance between consecutive layers can vary. The optimizationengine will typically pick the number of layers and the layer spacing tominimize irradiation time while maintaining limits that guaranteeaccurate beam position and dose application.

In order to determine the optimum number of layers and the spacingbetween them, the optimization engine may need to conduct varioussimulations of multiple candidate treatment plans with varying energylevels. For example, the optimization engine may start at 182 MeV andvary the energy level by a constant value or a multiple of the value,e.g., an increment of 5 MeV or multiples of 5 MeV (across multiplecandidate treatment plans) in order to determine the optimum number oflayers and spacing. It may not need to simulate every possible treatmentplan, because the optimization algorithm is configured to convergetowards the most optimal solution after a certain number of simulations.

Embodiments of the present invention are also configured to vary thespot positions and spot lateral spread (spot size) over the variouscandidate treatment plans when optimizing for efficiency. As mentionedpreviously, in conventional systems, the spot size and the grid sizeover which the treatment is delivered was typically fixed. Embodimentsof the present invention, however, allow for greater flexibility andefficiency by varying the spot size and spot position within acceptableranges over multiple candidate treatment plans. For example, given theshape of the target (and associated positional limitations), theoptimization engine can determine an optimal spot size and positions ofthe spots. As mentioned above, these parameters allow the treatmentplanning system to sacrifice the maximum resolution available for addedefficiency. In other words, the radiation therapy system can iterativelyadjust the spot positioning and spot lateral spread to find the optimalcombination of resolution and efficiency.

FIGS. 5A and 5B illustrate exemplary grids in the x-y plane within anenergy level over which proton therapy is delivered in accordance withan embodiment of the invention. As seen in FIG. 5B the spot lateralspread is larger than in FIG. 5A. Since the spot size and spot spacing(or spot positioning) parameters are related, the larger the spot size,the narrower the spacing required between the spots. Embodiments of thepresent invention allow the spot size and spot spacing to be set atoptimal levels so that the required dose can be delivered adequately inthe fastest amount of time possible. For example, in one particulartreatment, instead of delivering proton therapy at the two spots 510 and520 (shown in FIG. 5A), the treatment planning software may determine itis equally effective and more efficient to simply direct the dose energytowards a single larger spot 530. In such an instance, for example, theirradiation time may be minimized because the optimization engine allowsfewer, larger-sized spots to be irradiated as opposed to several,smaller spots.

In one aspect of the invention, the spot positions can be varied overmultiple candidate treatment plans so that the spots do not need toconform to a fixed grid. In conventional treatment systems, because thespot size and spot spacing parameters were fixed, the spots needed toconform to a fixed grid. Embodiments of the present invention allow thespot lateral spread and spot positioning parameters to freely vary(within acceptable ranges) between multiple candidate treatment plansand, therefore, there is no need for the spots to conform to a fixedgrid. For example, as seen in FIG. 5A, spot 585 does not conform to thefixed grid.

FIG. 6 is a table illustrating a plurality of candidate treatment plansand their results, wherein each cell of the table is representative of aradiation machine model implemented by the optimization engine todetermine irradiation time and plan quality for a respective treatmentplan in accordance with an embodiment of the invention.

As shown in the table of FIG. 6, the minimum MUs per spot and the energylayer spacing were adjusted to develop multiple candidate treatmentplans (for three fields, field 1, field 2 and field 3). Each of thecells indicated in the table shown in FIG. 6 comprises a result of asimulation associated with a respective treatment plan. Each cell, forexample, presents the time duration (number of seconds) it took for thetreatment delivery system to perform a respective simulation associatedwith the corresponding setting of energy layer spacing and minimum MUsper spot.

As seen in FIG. 6, grid 630 displays time-based results for all theassociated candidate treatment plans that met all the criteria fortreatment plan quality, e.g., PTV coverage, dose maximum, etc. The othercandidate treatment plans fail in one or more plan quality criterionFurthermore, out of those, grid 640 displays results for all treatmentplans that could be conducted in the shortest time periods whilemaintaining an acceptable plan quality. In this example, the treatmentplan(s) as conveyed by grid 640 would be the output of the optimizationengine 218.

The optimization engine 218 would, similarly, for all the degrees offreedom, conduct various simulations by varying the machine parameterswithin acceptable ranges to determine treatment times and correspondingplan qualities for various candidate treatment plans. In most instances,the optimization engine may not need to conduct every possiblesimulation, but may be able to converge to the most efficient solution(with acceptable plan qualities) using optimization algorithms.

As discussed in connection with FIG. 3B, the various degrees of freedomto vary 352 and the acceptable high low ranges for each parameter 354are used to generate a set of candidate treatment plans 356. Forexample, in the table of FIG. 6, candidate treatment plans would begenerated for each combination of minimum MUs per spot (e.g., 3 MU, 5MU, 8 MU and 11 MU) and energy layer spacing (3 MeV, 5 MeV, 8 MeV, and11 MeV). Each candidate treatment plan may have a different value foreach of the machine specific parameters (or degrees of freedom) butwithin a particular candidate treatment plan the value would be remainconstant.

Referring back to the table in FIG. 6, for example, a candidatetreatment plan may be generated that uses a minimum 3 MUs per spot withan energy layer spacing of 3 MeV—within this particular candidatetreatment plan the MUs per spot and energy layer spacing would be heldconstant. The simulator 362 of the optimization engine will subsequentlygenerate a treatment plan result 386 for one or more of the candidatetreatment plans. As mentioned above, in one embodiment, the optimizationengine may be able to converge on the most efficient solution withoutneeding to simulate each and every one of the candidate treatment plans.In other words, during treatment planning in the field, the optimizationengine may not need to generate results for each possible combination ofminimum MUs per spot and energy layer spacing.

Given the dosimetric criteria 370, the treatment plan selector 380 willthen select the most time-efficient treatment plan 382 from amongst theresult set. In the example of FIG. 6, resultant plan(s) as conveyed bygrid 640 would be determined to be the most time-efficient treatmentplan from amongst the result set.

In one embodiment, the treatment planning software may comprise agraphical user interface (GUI) that calculates and displays the fieldirradiation times during optimization. Further, the GUI may allow usersto place objectives and priorities on delivery time for each treatmentfield. Also, the GUI may allow the user to vary field-specific machineparameters in the optimization interface e.g., spot lateral spread,minimum number of MUs per spot etc.

FIG. 7 is a high-level software flow diagram illustrating the manner inwhich machine specific parameters are used in the treatment planningsystem to determine the most efficient treatment plans in accordancewith an embodiment of the present invention.

The treatment planning software system receives the machine parametersinto the beam data service module 732 from the machine parametersdatabase 772 into the service layer 730. The machine parameters are thentransmitted to the business layer 722 where the control systemcalculation module 712 performs the calculations of the varioustreatment plans with the machine parameters. The irradiation datadetermined by the control system calculation module 712 are transmittedto the application layer 702. In the application layer, the treatmentplanning system 704 uses the irradiation data to determine the treatmenttimes using treatment time calculation script module 708. Once treatmenttime calculation script module 708 determines treatment time, thetreatment planning system can select a treatment that has the mostefficient times while maintaining acceptable plan quality.

FIG. 8 is a flowchart depicting another exemplary process flow 800 fordetermining a resultant treatment plan for a proton radiation therapysystem based on given dose volume constraints, wherein the resultantplan is optimized for treatment time, in accordance with an embodimentof the present invention.

At step 802, the dose volume constraints and range information isaccessed, wherein the range information indicates acceptable deviationsfrom the dose volume constraints. As mentioned above, the minimum andmaximum range of machine specific parameters may also be provided to thetreatment planning software to perform the optimization.

At step 804, based on the proton radiation therapy system, machineconfiguration information is accessed comprising a plurality of machineparameters that define a maximum resolution achievable by the protonradiation therapy system in irradiating a patient. In other words, themachine parameters can be varied to achieve a maximum possibleresolution in irradiating a patient—however, the same parameters canalso be varied to deliver the treatment to the patient in the shortestamount of time by trading off the maximum resolution for efficiency.

At step 806, the optimization engine of the treatment planning softwareiteratively adjusts the plurality of machine parameters to generate aplurality of candidate treatment plans, wherein the iterativelyadjusting comprises adjusting the plurality of machine parameters tovalues which decrease the maximum resolution. Decreasing the maximumresolution, for example, may result in faster performance.

At step 808, the optimization engine simulates the plurality ofcandidate treatment plans with respect to the proton radiation therapysystem to generate a plurality of treatment plan results, wherein eachtreatment plan result comprises a respective treatment time and arespective plan quality. As shown in FIG. 6, for each treatment plansimulated, a time value can be obtained to indicate the amount of timeit would take to deliver the respective treatment.

Finally, at step 810, the optimization engine selects the resultanttreatment plan from the plurality of candidate treatment plans, whereinthe resultant treatment plan yields a treatment plan result comprising ashortest treatment time of the plurality of treatment plan results andan acceptable plan quality with respect to the dose volume constraints.

Embodiments according to the invention are thus described. Theseembodiments can be used to plan different types of external beamradiotherapy other than IMRT including, for example, image-guidedradiotherapy (IGRT), RapidArc™ radiotherapy, stereotactic bodyradiotherapy (SBRT), and stereotactic ablative radiotherapy (SABR).

Furthermore, embodiments of the present invention perform amulti-directional optimization that optimizes a treatment plan forefficiency by considering complex machine and beam-specific parameters(e.g., minimal application monitor units per spot, energy layer spacing,spot size and spot spacing, etc.) without sacrificing planquality—performing such a multi-directional optimization is beyond thecapability of a human and requires the use of a computing system.Embodiments according to the invention allow effective treatment planswith low treatment delivery times to be generated, which limit thepossibility of irregularities or inaccuracies in treatment deliveryresulting from patient movement. By optimizing for efficiency andtime-based constraints, embodiments according to the invention helpimprove the functioning of computing systems because they improve systemreliability and availability.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A computer implemented method of determining aresultant treatment plan for a proton radiation therapy system, themethod comprising: accessing dose volume constraints and a plurality ofmachine parameters associated with a proton radiation therapy system,wherein each of the plurality of machine parameters is operable to bevaried over a range, and wherein the plurality of machine parameters areassociated with beam characteristics of the proton radiation therapysystem; adjusting the plurality of machine parameters to generate aplurality of candidate treatment plans; simulating the plurality ofcandidate treatment plans to generate a plurality of treatment planresults, wherein each treatment plan result comprises a respectivetreatment time and a respective plan quality, and wherein the simulatingcomprises conducting one or more simulations using values selectedacross a respective range for each of the plurality of machineparameters; and from the plurality of candidate treatment plans,selecting the resultant treatment plan with a short treatment time andan acceptable plan quality with respect to the dose volume constraints.2. A method as described in claim 1, wherein the accessing the dosevolume constraints comprises accessing range information for the dosevolume constraints, wherein the range information indicates acceptabledeviations from the dose volume constraints.
 3. A method as described inclaim 1, wherein the plurality of machine parameters are operable to bevaried to achieve a maximum resolution achievable by the protonradiation therapy system in irradiating a patient, and wherein theiteratively adjusting comprises adjusting the plurality of machineparameters to values which decrease the maximum resolution.
 4. A methodas described in claim 1, wherein the plurality of machine parameterscomprise minimal monitor unit (MU) per spot.
 5. A method as described inclaim 4, wherein the plurality of machine parameters further comprises anumber of available energies and a step size between the availableenergies, wherein both the number of available energies and the stepsize between the available energies are operable to be varied.
 6. Amethod as described in claim 5, wherein an energy level of the availableenergies corresponds to a depth within a treatment region associatedwith a resultant treatment plan that a beam produced by the protonradiation therapy system can reach.
 7. A method as described in claim 5,wherein the plurality of machine parameters further comprises a spotlateral spread.
 8. A method as described in claim 5, wherein theplurality of machine parameters further comprises a spot lateral spread,wherein varying the spot lateral spread comprises varying a spot sizeassociated with a beam produced by the proton radiation therapy system.9. A method as described in claim 7 wherein the plurality of machineparameters further comprises a spot positioning.
 10. A method asdescribed in claim 7 wherein the plurality of machine parameters furthercomprises a spot positioning, wherein varying the spot positioningcomprises varying a spacing between spots associated with a beamproduced by the proton radiation therapy system.
 11. A method asdescribed in claim 1 wherein the acceptable plan quality is defined as aplan quality that deviates from the dose volume constraints within arange, wherein the range comprises acceptable deviations from the dosevolume constraints.
 12. A method as described in claim 1 furthercomprising: loading the resultant treatment plan into the protonradiation therapy system; and using the proton radiation therapy system,as configured by the resultant treatment plan, to irradiate a patient.13. A method as described in claim 1, wherein the accessing the dosevolume constraints comprises accessing range information for the dosevolume constraints, wherein the range information for the dose volumeconstraints indicates acceptable deviations from the dose volumeconstraints, wherein the range information for the dose volumeconstraints comprises: PTV coverage between a lower bound percentage andan upper bound percentage; and dose maximum below a bound percentage.14. A method as described in claim 1, wherein the proton radiationtherapy system is associated with external beam therapy methods selectedfrom a group consisting of: intensity modulated radiation therapy(IMRT), image-guided radiotherapy (IGRT), RapidArc™ radiotherapy,stereotactic body radiotherapy (SBRT), and stereotactic ablativeradiotherapy (SABR).
 15. A computer system comprising a processorcoupled to a bus and memory coupled to the bus wherein the memory isprogrammed with instructions that when executed cause the computersystem to implement a method of determining a resultant treatment planfor a proton radiation therapy system, wherein the method comprises:accessing dose volume constraints and a plurality of machine parametersassociated with a proton radiation therapy system, wherein each of theplurality of machine parameters is operable to be varied over a range,and wherein the plurality of machine parameters are associated with beamcharacteristics of the proton radiation therapy system; adjusting theplurality of machine parameters to generate a plurality of candidatetreatment plans; simulating the plurality of candidate treatment plansto generate a plurality of treatment plan results, wherein eachtreatment plan result comprises a respective treatment time and arespective plan quality, and wherein the simulating comprises conductingone or more simulations using values selected across a respective rangefor each of the plurality of machine parameters; and from the pluralityof candidate treatment plans, selecting the resultant treatment planwith a short treatment time and an acceptable plan quality with respectto the dose volume constraints.
 16. A system as described in claim 15,wherein the accessing the dose volume constraints comprises accessingrange information for the dose volume constraints, wherein the rangeinformation indicates acceptable deviations from the dose volumeconstraints.
 17. A system as described in claim 15, wherein theplurality of machine parameters are operable to be varied to achieve amaximum resolution achievable by the proton radiation therapy system inirradiating a patient, and wherein the iteratively adjusting comprisesadjusting the plurality of machine parameters to values which decreasethe maximum resolution.
 18. A system as described in claim 15 whereinthe acceptable plan quality is defined as a plan quality that deviatesfrom the dose volume constraints within a range, wherein the rangecomprises acceptable deviations from the dose volume constraints.
 19. Acomputer implemented method of determining a treatment plan for protonradiation therapy that is optimized for treatment time, the methodcomprising: accessing information defining patient anatomy, dosimetriccriteria and delivery system properties, wherein the dosimetric criteriacomprises dose volume constraints and range information, wherein therange information indicates acceptable deviations from the dose volumeconstraints; based on a proton radiation therapy system, accessingdelivery system properties comprising a plurality of machine parametersthat configure the proton radiation therapy system to achieve aresultant resolution, wherein the plurality of machine parameters areassociated with calibration criteria of the proton radiation therapysystem, and wherein the delivery system properties comprises a range foreach of the plurality of machine parameters; adjusting the plurality ofmachine parameters to generate a plurality of candidate treatment plans;simulating the plurality of candidate treatment plans to generate aplurality of treatment plan results, wherein each treatment plan resultcomprises a respective treatment time and a respective plan quality, andwherein the simulating comprises conducting one or more simulationsusing values selected across a respective range for each of theplurality of machine parameters; and from the plurality of candidatetreatment plans, selecting the treatment plan with a shorter treatmenttime and an acceptable plan quality.
 20. The method as described inclaim 19, wherein the iteratively adjusting comprises adjusting theplurality of machine parameters to values which decrease a maximumresolution achievable by the proton radiation therapy system.